Method for analyzing image data relating to agglutination assays

ABSTRACT

A method for analyzing a digital image containing the result of an agglutination assay to generate a quantitative result value representative of the degree of agglutination of the sample is provided. The method for analyzing the digital image includes: applying a filter to extract a component of the image or portion of a spectrum where a signal to noise ratio between agglutinated and background is maximized; extracting a set of features that characterize the agglutination pattern, obtaining a quantification function which maps measured features to the actual concentration of the sample and computing a quantitative result for each sample in the assay.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to a method for automaticanalysis of agglutination assays and more specifically, provides a fast,simple and automatic diagnostic system which does not require expensiveequipment and enables registration of an agglutination assay and itsresult for later use. Furthermore, the system is suitable for screeningand batch processing multiple samples.

2. Description of the Related Art

Agglutination reactions are valuable analytical tools that can beapplied to many reaction systems in which multivalent binding betweenreactants is possible. A particle agglutination immunoassay isadvantageous in that it requires only the mixing of a sample to betested with a suspension of insoluble carrier particles (e.g., latex)sensitized with an antibody or an antigen. Typical examples areimmunoassays which generally involve:

a) mixing a sample containing an antibody, with an antigen(corresponding to the antibody on the sample) and observingimmunocomplex formation;

b) mixing a sample containing an antigen carrying at least two antigenicfunctions (bivalent or multivalent antigen) with the correspondingantibody and observing immunocomplex formation;

c) mixing monoclonal antibodies with a sample containing at least twodifferent monovalent antigens and observing immunocomplex formation;

d) for any of the reactions mentioned above, coupling the antigen orantibody to particles, such as latex particles, colloids, etc.; andobserving immunoagglutinate formation.

e) in the case of rapid plasma reagin (“RPR”) type reactions (e.g., forsyphilis serodiagnostic), adding carbon as a visualizing agent andobserving flocculation reactions;

f) for any of the reactions mentioned above, applying the above steps toantigens present on cell surfaces in which case the number of antigensper physical unit is normally a hundred or more, and in which case suchcells may be agglutinated by monoclonal antibodies even if each antigenmolecule is monovalent.

Such reactions are typically observed on the surface of a solidsubstrate such as a glass or plastic plate, or on the well of amicrotitre plate. The solid surface is preferably colored to contrastwith the color of the agglutinate.

The formation of visible agglutinates depends on the ratio ofantigen/antibody. If a constant amount of antibody is mixed withincreasing amounts of antigen three situations can be identified:

A pro-zone phenomenon, characterized by the presence of excess antibodyin the test system and which is further characterized by non-occurrenceof any visible phase reaction due to the inhibition of agglutinateformation by the excess antibody.

An equivalence zone characterized by the presence of antigen andantibody in optimum proportions and which is further characterized byenhanced agglutinateformation and visible phase reactions.

Post-zone Phenomenon characterized by the presence of excess antigen inthe test system and which is further characterized by the nonoccurrenceof visible reaction.

Pro-zone and Post-zone phenomena may be corrected by making serialdilutions of serum, thereby reducing the concentration of antigen orantibody in the test system, and optimizing the concentrations ofantigen and antibody.

Agglutination reactions may also be performed with any set of moleculeswhich bind to each other, provided that each of the reactants has atleast two binding sites, or is coupled to a particle or otherwise linkedtogether so that two or more binding sites per physical unit is created.Examples of systems other than antibodies/antigens that may formagglutinates are (poly)carbohydrates/lectins, biotin or biotinylatedcompounds/avidin or streptavidin, corresponding sequences of nucleicacids, any protein receptor and its corresponding ligand etc.

It can be appreciated that agglutination assays have been in use foryears. Today, many agglutination assays are available to physicians fordiagnosing various diseases, and an increasing number of such assays donot require that the patient's sample (e.g. blood, urine, saliva, stool)be sent to a diagnostic laboratory for analysis. Such in-office assaysenable results to be obtained quickly and entered it to the patient'scomputer record. Test results can also be available for physicians inthe emergency room.

Agglutination-based products for detection and quantification ofanalytes have been produced for a wide range of analytes. Very early onin the field, products were developed for the detection of humanchorionic gonadotropic hormone (HCG) in urine, for the diagnosis ofpregnancy. Two different principles were used: 1. products were madewith antibodies on a particle surface, which gave agglutination in thepresence of the analyte; and 2. products were made with antigen on thesurface of the particles, and a reagent containing antibodies was addedtogether with the test sample. In this second variant, agglutinationtook place in the absence or at low concentration of the analyte as ahigher concentration of the analyte complexed with the antibodies andhindered the agglutination.

Furthermore, agglutination reagents for testing for drugs, includingprescription drugs and most illegal drugs, and many non-proteinaceoushormones, such as testosterone, progesterone, oestriol, have been made.

The application of agglutination reactions is not confined to human orveterinary diagnostics, they function as great tools in other fields aswell, including the agricultural industry, for detection of plantdiseases (virus, bacteria and fungi) and industries which requiremonitoring of processes (e.g., the various food industries and thelike).

It should be noted, however, that the examples given above are notconsidered to be a complete listing of the applications of agglutinationassays and many other applications are possible.

Typical protein analytes for agglutination technology include C-reactiveprotein (CRP), transferrin, albumin, prealbumin, haptoglobin,immunoglobulin G, M, A and E, apolipoproteins, lipoproteins, ferritin,thyroid stimulation hormone (TSH) and other proteinaceous hormones,coagulation factors, plasminogen, plasmin, fibrinogen, fibrin splitproducts, tissue plasminogen activator (TPA), betamicrogobulins,prostate-specific antigen (PSA), collagen, cancer markers (e.g. CEAandalphafoetoprotein), and several enzymes and markers for cell damage(e.g. myoglobin and troponin I and T).

Moreover, many agglutination test kits for infectious diseases have beenmade, including mononucleosis, streptococcus infection, staphylococcusinfection, toxoplasma infection, trichomonas infection and syphilis.

Such reagents and reagent sets are either based upon detection of theinfectious agent itself, or detection of antibodies produced by the bodyas a reaction to the infectious disease.

A typical medical technique for agglutination assay consists of mixing asample with one or more agglutination reagents. Binding sites on theagglutination reagent(s) bond to corresponding sites on components ofthe sample, if present, and this binding results in agglutinates, whichare visible clusters of bonded reagent and sample component. Thus, adesired reagent may be mixed with a sample and the presence ofagglutinates in the mixture indicates the presence of the correspondingcomponent in the sample. So a commercial latex particle agglutinationtest on slide it's used (like Toxocell Latex, made by Biokit ofBarcelona, Spain).

A typical latex agglutination slide-based test, such as Toxocell Latex,made by Biokit of Barcelona, Spain is described below.

Primary Screening: Serum sample and latex reagent are mixed with awooden stick on a slide section for approximately 5 minutes. Samplesolvent is used as a negative control. After that, the slide is watchedunder direct and intense light for the presence or absence ofagglutination. Results are recorded according to: positive reaction (3+large aggregates on clear background, 2+ medium-sized aggregates on aslightly cloudy background, 1+ small aggregates on a cloudy background(for assay purposes, aggregates barely visible on milky background maybe considered a positive indication) or negative reaction (absence ofagglutination: milky uniform aspect).

Titration technique: the sample undergoes two serial dilutions withsample solvent over a single slide and is processed in accordance withthe primary screening method described above. However, a titre of agiven sample corresponds to the highest serum dilution that stillpresents a clearly visible agglutination (+1 according to the abovescale). Provided that samples are tested in two fold dilutions, the realconcentration will be in the range from that of the first and seconddilution. With this technique, there is no way to determine the realconcentration and in the technique is thus, useful in cases where it isthe variation of concentration as opposed to the real concentration,that is of interest.

Although traditional agglutination reactions are, in fact, quantitativein nature, the interpretation of the result is traditionallyqualitative.

However, since many of the analytes which may be the subject of suchagglutination reactions are desired to be measured quantitatively, otherand more complicated methods like ELISA, RIA, immunofiltration orimmuno-chromatography methods have been used.

A few patents try to address the problem of quantitative vs. qualitativeanalysis in the context of agglutinate assays by the application ofautomatic procedure to a scanned image of the agglutinate assay. PCTInternational Application WO0005571, describes a device and method forthe quantification of agglutination reactions based on a digital imageof the agglutination reaction. Although this patent presents exampleswhere samples with different concentrations produce different values inthe measured features, no evidence is presented towards thereproducibility of such analysis. In some of the examples the obtainedcurves of measured features versus concentration have extremely smallregions where quantification may be possible. Last but not least, thepatent does not study the reproducibility of the process which ismandatory for automatic and general diagnostic systems. In severalcases, agglutination patterns include great variations which are notaccounted for when using these kinds of systems (see FIG. 2). The methodproposed by PCT International Application WO0005571 does not addressthis problem. Since no preprocessing is applied to the image beforefeature extraction, great variations in the process can occur due to nonuniform illumination, noise, dirt and bubbles, in addition to variationsin the sample itself. Further, the procedure to obtain the reaction iscompletely manual, i.e., a wooden stick is used for the mixing ofreagents and distribution thereof over a plate surface. This actuallyworsens the distribution of the agglutinates, as the particles will beformed erratically on an irreproducible fashion, causing the particlesto increase in height. Accordingly, so any results obtained aretypically inaccurate.

In U.S. Pat. No. 5,541,417, a similar method is proposed. A digitalimage of the agglutination reaction is obtained and processed. Thequantification is based on a roughness index that captures the pixellocal variations. These kinds of texture descriptors are efficient whendealing with uniform textures. However, as noted above, in the generalcase of agglutination reactions, great variations in the agglutinationpatterns are expected. As said before, these variations are oftenrelated with sample characteristics that can not be inferred a priori.Therefore, the roughness will produce different output values forsamples with equal concentrations but different agglutination patterns.In a second embodiment the patent proposes to use a neural network togeneralize local intensity variations. Neural networks must be trainedusing a sufficiently rich training data. This is a limitation indiagnostic systems where reagents may change over time, producingvariations in the agglutination patterns. Therefore, every time changesare made to the system the network must be trained and sent to all theusers of the system. On the other hand, as mentioned above,agglutinations with similar concentrations may have differentagglutination patterns (see FIG. 2) and a neural network based only on aroughness measure will have problems to accurately quantify the sample.

Another example of the application of this technique involves bloodgroup serology, where agglutination is the result of mixing red cells(containing a particular antigen) with a serum containing thecorresponding antibody. Blood typing tests are done before a personreceives a blood transfusion and to check a pregnant woman's blood type.Human blood is classified, or typed, according to the presence orabsence of certain markers (called antigens) on the surface of red bloodcells. The most important antigens are blood group antigens (ABO) andthe Rh antigen. Therefore, the two most common blood typing tests arethe ABO and Rh tests.

With respect to this type of agglutination assay, PCT InternationalApplication WO8907255 attempts to solve a normal operation problem withthe automatic detectors for hemoagglutination test. In the normalprocedure, the way in which the agglutinate deposits over the walls andbottom of the sample cell (or plate depression), make the automaticdetection of the agglutination difficult. Normally a small button isformed on the centre of the reaction surface, and other deposits arescattered unevenly across the surface. The method appears to solve thoseproblems only in cases when a positive/negative detection is needed,comparing the absorbance values of the centre and periphery zones of thebottom of the cell (or plate). However the described method cannot beapplied to diluted samples, usually found when the analyte concentrationis low, and/or the absorbance difference between both zones is low andeach absorbance value is close to zero (because the high dispersion ofthe agglutinates).

Thus, traditional agglutination assays have been carried out only semiquantitatively, and the interpretation of results obtained therefrom aresubject to human error inaccuracy and are often not reproducible.

SUMMARY OF THE INVENTION

To overcome some of the disadvantages described above, there is providea method for acquiring a digital image of an agglutination resultcomprising performing an agglutination assay on a reaction substratehaving a set of dimensions and characteristics which permit a pattern ofagglutination in a result of said assay. The image of the result isdeveloped. The image has a colored background which maximizes a signalto noise ratio. Further, the image has been passed through a filter thatcomplements an action of the colored background and enhances theagglutinates in the image while additionally increasing the signal tonoise ratio.

In another specific enhancement the pattern is related to aconcentration of interest in the sample.

In another specific enhancement the image is acquired using a flatbedscanner.

In another specific enhancement the image is acquired using a camera.

In another specific enhancement the image is acquired using an array ofphotodetectors.

Another aspect of the invention is a method for analyzing anagglutination assay comprising performing an agglutination assay bymixing a volume of a sample dilution with a corresponding volume of atleast one reactive and observing a formation of agglutinates. A negativecontrol is prepared by mixing a second volume of the sample dilutionequal to the volume of sample dilution with a second correspondingvolume of the reactive. A digital image of the sample dilutions isobtained. The digital image is processed against a calibration curve togenerate a quantitative result value representative of a degree ofagglutination of the sample and reactive.

In another specific enhancement the method further comprises processingthe image by automatically identifying and quantifying the samplespresent in the digital image of the assay.

More specifically, the method comprises extracting a set of features. Aquantification function which maps measured features to the actualconcentration of the sample is obtained. A quantitative result for eachsample in the assay is extracted.

Yet another aspect of the invention, is a method of extracting a set offeatures that characterize a set of agglutination results, the methodcomprising applying a digital or optical filter to extract a componentof the image or portion of a spectrum where a signal to noise ratiobetween agglutinates and background is maximized. A top-hat transformwith a spherical structural element is applied to obtain a new imagereferred as Rd and assign the set of pixels to within each well to amatrix referred as Rdi. The pixels within a well produced by theagglutination reaction are extracted. A mean of the pixels in Rdi iscomputed. A standard deviation of the pixels in Rdi is computed. Atleast one other statistical and texture descriptor are computed. Anestimated area of the agglutinates is determined.

In another specific enhancement the statistical and textual descriptoris at least one of Media, Absolute Deviation, Kurtosis and moments ofco-occurrence Matrices.

Another aspect of the invention is a method for obtaining aquantification function F for a dilution which maps a set of measuredfeatures to an actual concentration of the sample, the method comprisingmaking a table with entries: Dilution, Mean and Concentration (UI/ml). Aleast squares analysis is applied to a region above a point of negativeresponse and below a point of saturation to adjust a parametric functionto the table. Safeguard thresholds are obtained for indicating abeginning and an end of a quantification zone and confidence weights. ATh+ value, that defines a set of values of a feature that corresponds topositive agglutinations is obtained.

Another aspect of the invention is a method for extracting aquantitative result for each dilution of a sample based on itsagglutination features, a quantification function and a set ofdilutions, the method comprising. For each agglutination feature (fi),fi is compared with an obtained Th+ value that defines values of afeature that corresponds to positive agglutinations. If fi>Th+, adilution is declared as positive. For a last positive dilution (fj), iffj falls within a quantification region, its concentration Q isestimated as: Q=F(fj)*j. Th+, fj and Q values are validated if an areameasure is above a threshold of a negative control and below saturation.A final result is obtained by determining a weighted average of thevalidated values.

Another aspect of the invention is a method of evaluating anagglutination assay comprising obtaining a semiquantification table Fusing a set of labeled patient samples. A set of pattern recognitiontechniques are applied to determine a set of thresholds which divideeach agglutination into an agglutination class with a minimumprobability error. A threshold Th+ which divides positive and negativeagglutination reactions is obtained. For each patient sample, asemiquantification process is obtained for an evaluation of theagglutination.

More specifically, a semiquantification training table F is obtainedusing a process comprising for each image in a training sample,processing all reactions with the feature extraction method to obtain aset of agglutination features. A table is made with entries for GivenAgglutination Score and Feature.

More specifically, a semiquantitative result for evaluating anagglutination assay is obtained using a method comprising measuring afeature that characterizes the agglutination. A threshold (Th+) isobtained. For each positive reaction, the semiquantification table F isused to obtain an agglutination score.

BRIEF DESCRIPTION OF THE DRAWINGS

Various other objects, features and attendant advantages of the presentinvention will become fully appreciated as the same becomes betterunderstood when considered in conjunction with the accompanyingdrawings, in which like reference characters designate the same orsimilar parts throughout the several views, and wherein:

FIG. 1 is a scanned image of the plate used in Example 1. At theupper-left corner is a negative control. From the bottom-right and up isa set of dilutions for a given sample. Starting from a 1:1 dilution 1:2,1:4, 1:6, etc., dilutions were produced.

FIG. 2. Shows agglutination results for three samples with almost equalconcentrations but different agglutination patterns. From bottom to top1:1, 1:2, 1:4, 1:8, and 1:16 dilutions are shown.

FIG. 3 shows the results for a set of images of standard (samples) and apiecewise linear fit to this data for quantification.

FIG. 4 shows a digital image with the agglutination reactions,containing four blood typing tests.

FIG. 5 shows Table 1, which includes the details of procedures that arediscussed herein.

FIG. 6 shows Table 2, which includes the results of each dilution.

FIG. 7 shows Table 3, which includes results for Example 1.

FIG. 8 shows Table 4, which includes results for Example 2.

FIG. 9 shows Table 5, which includes results for Example 2.

FIG. 10 shows Table 6 which includes results obtained with proposedsemi-quantification process with the results given by a human expert.

FIG. 11 shows Table 7 which shows a confusion matrix with classificationresults.

DETAILED DESCRIPTION OF THE INVENTION

Turning in greater detail to the drawings, in which similar referencecharacters denote similar elements throughout the several views, theattached figures illustrate an apparatus and a preferred process for theautomatic quantitative analysis of agglutination assays, whichcomprises:

a. A digital image acquisition device to acquire a digital image of theagglutination reaction, and

b. a set of data processing procedures that process the digital image toautomatically obtain a quantitative result of the agglutinationreaction.

The image acquisition system can be a desktop flat bed computer scanner,or any other imaging device such as a digital camera, video camera, orany other array of light detectors.

The data processing may be carried out by a set of proceduresimplemented in a personal computer but other more specific digitalsystems as custom electronics can be used.

The digital image may have an arbitrary number of channels, directlyacquired by the acquisition system or produced with other methods likeoptical or digital filtering and/or processing.

The assay is performed on any suitable substrate such as a plasticplate. In order to hold each sample in a confined area, the substratemay contain predefined wells of appropriate shape and dimensions. Thisfacilitates the identification of each sample and fixes the height ofeach sample given the volumes of sample and reagents

For example, the surface of the reaction plate may be shaped so that thereaction mixture is enclosed within a distinct region in order toimprove reproducibility in quantitative readings. T his may be achievedby a circular elevation in a plastic surface, which can be madeaccording to standard production methods, or by the use of a microtitreplate.

The determination of the quantitative result may involve the extractionof a set of features from the digital image. These features characterizethe pixel distribution for each sample in the assay and relate them witha quantitative result.

As an example of the use of the invention, the procedure is applied to acommercial latex agglutination kit for quantitative determination ofantibodies to a certain protozoan in serum. Other uses of the mentionedtechnique include any particulate agglutination reaction where a reagentsuch as latex is used; hematies agglutination; bacterial agglutinationand RPRs, wherein carbon particles are used as contrasting media whichallows for the observation of the agglutination, in which the aggregatesand the media are transparent; etc.

After the acquisition of the image, the proposed software implementationof the present procedure gives a quantitative representation of theanalyte concentration which can be stored for later reference.

The quantitative automatic determination of the results for each samplein the assay may involve the following steps:

1. the determination of the areas of the digital image that correspondto each sample agglutination result,

2. the extraction of the set of pixels which represent the agglutinationresults,

3. the computation of a set of features to determine the quantitativeresult.

The extraction of the portion of pixel actually belonging to theagglutination can be determined by clustering methods that groupneighboring pixels of similar features such as gray level, color,texture, etc. [M. Sonka, V. Hlavac, R. Boyle. “Image Processing,Analysis, and Machine Vision”, 1999, Second Edition, ITP Publishing.].The clusters may be also used for the determination of the quantitativeresults, for example computing their area, shape, color distribution,number, etc. Alternatively, the agglutination regions can be extractedwith methods of mathematical morphology such as opening and closingfollowed by thresholding [M. Sonka, V. Hlavac, R. Boyle. “ImageProcessing, Analysis, and Machine Vision”, 1999, Second Edition, ITPPublishing].

The image acquisition system will preferably use a controlled anduniform light source and possibly a colored background in order toenhance the differences between the agglutination and the background.Even in the case of the use of a flat bed scanner or other controlledlight sources, it is important to calibrate the system. For that end, apredefined calibration object may be used. The calibration object may beused before, or at the same time, of the acquisition of the assay image.In either case, the data processing system will compare the image of thecalibration object with a stored reference of it. The relationshipbetween both images can be used to translate the obtained pixel valuesinto a reference coordinate system or to reject the image if thelightning conditions cannot guarantee the reliable extraction of theagglutination results.

The same calibration object, or another, can be used to determine theintrinsic parameters of the acquisition system such as magnification,and other geometrical and color distortions. The use of a calibrationobject will provide a relationship between pixel image characteristicsand real dimensions, positions and colors.

Although the system described uses standard white light, it may also useanother type of light and photodetectors intended to extract informationin other parts of the spectrum such as the infrared or ultraviolet. Thesystem may also use a set of optical and/or digital filters to enhancesome portions of the spectrum.

The data processing procedures of the invention are intended toautomatically identify and quantify the samples present in the digitalimage of the assay. The method includes the following steps:

1. determination of the position of the assay substrate in the image,

2. extraction of the areas of each sample,

3. extraction of the pixels within the previous image that correspond tothe agglutination result,

4. extraction of a set of features that characterize the agglutinationresults, and

5. extraction of a quantitative result for each sample in the assay.

Determination of the Position of the Assay Substrate in the Image

The extracted areas of each sample are examined for regions of knownshape, corresponding to the predefined wells, being colored differentlyfrom that of the background. The use of well-chosen colored backgroundsfacilitates this step (discussed below). Additionally, the geometry ofthe assay substrate can be used to locate potential positions ofsamples. To detect wells containing samples, the image is pre-processedwith a grey level opening with a spherical structure element and is thenthresholded [M. Sonka, V. Hlavac, R. Boyle. “Image Processing, Analysis,and Machine Vision”, 1999, Second Edition, ITP Publishing.]. Thisprocessing fills the gaps in the sample within the well and increasesthe contrast between samples and background. Using the known shape ofthe wells, the results of this step can be further improved with a localshape matching procedure.

The pixels corresponding to the agglutination are extracted applying thetop-hat transform with a spherical structural element [M. Sonka, V.Hlavac, R. Boyle. “Image Processing, Analysis, and Machine Vision”,1999, Second Edition, ITP Publishing.]. This process subtracts thebackground and enhances the agglutinates to obtain an image withenhanced agglutinates. The resulting digital image of top-hat transformis referred to as IMD. The pixels corresponding to the agglutination canbe then obtained via thresholding. When performing the opening operationthe crests of the obtained texture in the digital image are extracted.This texture is caused by the agglutination and is mainly determined bythe appearance of agglutinates. These agglutinates reflect the lightprojected by the light source and appear as bright spots (crests) in thedigital image. Applying the previously mentioned processing agglutinatesare successfully extracted while canceling non-uniform background.Without this step, most of the features computed from the agglutinationwould be distorted by the non-uniform background and will not generalizeto other cases.

Extraction of a Set of Features that Characterize the AgglutinationResults

For each well containing a sample, a set of features that characterizethe agglutination are measured. In some cases the agglutination mayconsist of large agglutinates with high contrast against the backgroundin other cases the agglutination results in almost uniform texturedareas. In the former case, the detection may be accomplished bythresholding techniques followed by the determination of the area,number, etc, of the agglutinates, if only the detection of theagglutination is of interest. For the later case, a set of features thatcharacterize the texture is should be identified for detection andquantification of the sample.

The set of features used for the classification of the texture may becomposed of one or more of the following features in addition to others:statistical moments of the agglutination pixels (mean, standarddeviation, kurtosis, etc), moments of the co-occurrence matrices,fractal signatures, spectral features such as Fourier spectrum, etc. [M.Sonka, V. Hlavac, R. Boyle. “Image Processing, Analysis, and MachineVision”, 1999, Second Edition, ITP Publishing.].

Agglutination Feature Extraction Using the Top-Hat Transforms

To take into account the inherent variability of the technique, it ispreferred that the method must be robust and capable of dealing withoutliers and defects caused during the reaction manipulation such asbubbles in the sample, stains, colored threads, etc., and inherentproblems of the acquisition technique such as uneven illumination. Forthat end the method applies a preprocessing step before computingfeatures that eliminates these artifacts. As said before, unevenillumination is cancelled while enhancing the agglutination viamathematical morphology.

The sample image is processed with a top-hat transform (For a detailedexplanation of the top-hat transform see [M. Sonka, V. Hlavac, R. Boyle.“Image Processing, Analysis, and Machine Vision”, 1999, Second Edition,ITP Publishing.]). This process subtracts the background and enhancesthe agglutinates. After that a mean is computed over the processessample image, which is shown to be an effective feature. Since thevolume of the well is set in order to obtain a thin layer of sample, themean is a good estimator for the strength of the agglutination, and inthis way can be related to the concentration of the sample.

Bubbles can similarly be detected. Artifacts with colors similar to theones of the agglutinates may be detected and removed observing theirshapes and sizes.

It is desirable to have an optimum total volume in each well for theextracted features to successfully work as a quantitative feature.Ultimately, the total volume is related to several physical andmechanical properties (size of the reagent particles, well diameter,agitation, etc) of the invention. These parameters should be set andadjusted for practicing the remaining steps of the proposed process.Parameters may be set as follows:

In order to enhance the contrast between the agglutinates and thebackground a colored background may be used. Selection of the backgroundcolor depends on the wavelength of the reflected light by theagglutinates (for example: latex particles).

The volume and dimensions of the well should be selected based on thenext criteria. The well should be rounded enough to avoid overlappingand accumulation of agglutinates on corners (in the case that arectangular or square well it's used).

The volume of the well it's limited by the access to low volumemicropipettes, to avoid the unnecessary excess of costly reactive, andthe user ability to handle the small reactives volumes on a small space(avoiding spontaneous mix). Normally a volume between 30 and 120 μL it'sadvisable.

Also the dimensions of the polystyrene slide are limited by the capturearea of the digitalization device, at least 2 well should be on the sameimage (one for the sample and the other for the negative control)

The diameter should be enough to avoid the spontaneous overlapping ofsample and reactive until the operator does it by the use of the stick.Also it may allow the introduction and extraction of the micropipettewithout problems to easy the work of the operator.

Since the volume of the well is set in order to obtain a thin layer ofsample, the height should be choose taking into account all thepreferences selected previously.

The parameters used during the assay and the quantification processshould guarantee that the reaction to be quantified fall within theregion where the measured features and the resulting concentration havea known relationship. That is above the negative reaction zone and belowthe saturation level.

Special care should be taken with the control of the ambient temperatureand the analyte sample concentration. High ambient temperature couldincrease the agglutination affinity, so it's advisable to run theanalysis on a room preferably at 25° C.±5° C.

If the data provided by the patient indicate that the analyte has a highconcentration or agglutination affinity (in any case by a previouslyanalysis by the known slide-based test), it's advisable to dilute thesample until the supposed concentration falls between the negativereaction zone and below the saturation level. Others parameters, as theambient atmospheric pressure have low impact but always it's preferableto try the analysis on a closed room (to avoid dust contamination of thesample). As always it's advisable that the operator it's fully trained.

In this way, the captured image has correlation with the actualconcentration of the sample.

If all previous conditions are met, the extracted features, for examplethe mean, have a strong correlation with the amount of agglutination,and therefore can be used as quantification measures. Quantificationusing a set of features that characterize the agglutination results

With the set of features for each sample, results can be quantified. Forquantification, the measured features are compared with the ones of aknown standard. That is, the set of features measured are used asargument in a quantification function or table to obtain thequantification result. This quantification result can be evaluated by anordinary skill in the art using methods known in the art.

To obtain the quantification function or table, samples with knowntitles are processed with the same technique. Several assays may be usedto improve the results such as improved signal relation ratios, thattake into account intrinsic technique variability, and make possible,the generalization of the results while avoiding overfitting.

With the obtained results, least squares, robust least squares, oranother method [R. Burden, J. Faires. “Numerical Analysis”, 2002,Thomson.] may be used to fit a quantification function to the obtainedresults for the standard sample.

The measure features for the sample are uses as arguments for thequantification function to provide quantification of unknown samples.Several features and quantification functions may be used to improve theresults. For that end, techniques of classifier combination may be used.For instance, the quantification of several dilutions can be averaged toimprove the results, or consider a combination of a set of features. Inboth cases confidence weights may also be used. In some cases, it isalready known that the method has more sensitivity with some dilutions,etc. These confidence weights can be obtained together with the leastsquare process mentioned above.

This calibration procedure can be performed by the technician at thelaboratory since no extra equipment is needed.

Three examples of basic procedures comprising the present automaticquantification process are provided below.

Procedure 1: Feature Extraction

For each well containing an agglutination reaction, the procedure forthe extraction of the features characterizes the agglutination resultperforming the following steps:

-   -   1. Apply a digital or optical filter to extract the component of        the image or portion of the spectrum where the signal to noise        ratio between the agglutinates and background is maximized. For        example, an optical filter can be placed between the sample and        the sensor to capture the desired portion of the spectrum, or        the responses of the multichannel sensor as a color array may be        combined to extract the desired component.    -   2. Apply the top-hat transform.    -   3. Feature extraction. In this step only pixels within the well        are considered, that is, only pixels produced by the        agglutination reaction. The set of pixels within the well is        herein referred to as Rdi. Compute the mean of the pixels in        Rdi: Mean=mean(Rdi).        -   a. Computer the standard deviation of the pixels in Rdi:            Std=std(Rdi).        -   b. Compute other statistical and texture descriptors: Median            Absolute Deviation, Kurtosis, moments of the co-occurrence            Matrices, etc.        -   c. Estimate the area of the agglutinates.            -   i. Extract the agglutinates.            -   ii. Estimate the area of the agglutinates, Area.                Procedure 2: Obtaining the Quantification Function F.

To obtain the function that maps measured features to the actualconcentration of the sample, a set of assays is performed on a standardsample with known concentration. To build the quantification function, aset of predefined dilutions is performed for each sample.

-   -   1. For Each Standard Assay (Image):        -   a. Process all standard dilutions according to Procedure 1            to obtain the agglutination features of each dilution.        -   b. Make a table with entries: Dilution, Feature,            Concentration (UI/ml).    -   2. Apply least squares, robust least squares, or other method to        adjust the desired quantification curve F (for example: a        piecewise linear function) to the data previously obtained in        (1).    -   3. Obtain safeguard thresholds that indicate the beginning and        end of the quantification zone and confidence weights.        Procedure 3: The Quantification Process

For the quantification, all dilutions of the sample are processed. Foreach dilution, the features that characterize the agglutination aremeasured. Suppose that dilutions 1, 2, 4, 8, 16, etc. have been done andfeatures: f1, f2, f4, . . . , f16, . . . etc. have been obtained. Alsoconsider a quantification function F that maps this feature into itsconcentration (see Procedure 2).

-   -   1. If fi>Th+ the dilution is declared as positive. Th+ is        obtained together with the function F and defines the values of        the feature that correspond to positive agglutinations.    -   2. Let fj be the last positive dilution.    -   3. If fj falls within the quantification region, concentration Q        can be estimated as:        Q=F(fj)*j.  a

To validate the measure, the area of the agglutinates may be used.Negative reactions have small agglutinates. Since a negative control isused, samples can be evaluated considering the area differences. Bigdifferences indicate a positive result. This must be done since thefeatures tend to saturate at high concentrations.

To make a more robust estimation, the average of all dilutions fallingwithin the quantification zone is computed. They can be determined fromthe last positive dilution using suitable safeguard threshold obtainedtogether with the function F.

EXAMPLES Example 1

The following example was carried out by realizing a Toxoplasmosisagglutination reaction.

Sample Requirements for the Toxoplasmosis Test:

Human serum is collected by centrifugation from clotted human blood,obtained from vein puncture. Preservatives agents should be avoided. Ifthe test is not carried out on the same day, the serum should be storedat 4° C. for a maximum of 48 hrs; for longer periods, it is advisable tofreeze the sample.

The reaction may be performed over transparent polystyrene slides, withreaction areas delimited by ledges of the same material defining acircle.

Materials:

-   -   Automatic pipettes of 30 and 60 μL.    -   Disposable tips.    -   Orbital shaker (speed between 80 to 90 rpm).    -   Thermometer for measuring from 32.00 to 122.00° F. (able to        allow the measure of tents of degree).    -   Sample Solvent: 8.5 g/l NaCl, 1 g/l BSA (bovine seroalbumine), 1        g/l sodium azide, 1 liter H₂O sqf.    -   Chronometer.        Latex Reagent

The dilution of a latex reagent should be adjusted previously inaccordance with World Health Organization standard serum. This isaccomplished by applying standard slide technique, so that the lastdilution in which a positive agglutination is detected, corresponds to aconcentration of 10 IU/ml (international units by ml of serum).

Sample Preparation:

The sample should be thawed and allowed to reach room temperature,before use. Before performing a set of determinations, latex reagent,controls and solvent should reach a room temperature. The assay shouldbe carried out at a temperature between 68.00 to 86.00° F. The latexreagent should be shaken gently before use (avoiding foamingproduction).

Primary Screening:

-   -   1. Place a previously determined volume A of serum by means of        an automatic pipette on one end of the slide hollows.    -   2. Add a previously determined volume B of latex reagent on the        opposite end.    -   3. Mix both drops with a stirrer, covering the whole surface,        allowing the liquid to reach the border of the cavity.    -   4. Place a previously determined volume A of the sample solvent        and mix it with a previously determined volume B of latex        reagent on the number 20 section of the slide. It will be use as        negative control (FIG. 2).    -   5. Rotate the slide at 80 rpm for 5 minutes.    -   6. Obtain the digital image with the associated software against        a previously determined dark background, whose color it is        selected to increase the definition of the edges of the        aggregates.    -   7. If the results are positive, a titration should be done to        obtain the analyte serum concentration.        Titration:

Sample dilutions should be done over the same slide as follows:

-   -   1. Add a volume A of sample diluent on each slide section    -   2. Place a volume A of the sample on section 1.    -   3. Mix it with the solvent previously placed.    -   4. Using the same pipette take in and release serum and diluents        until they are mixed well (e.g. a 50% dilution)    -   5. Take a volume A of that dilution and transfer it to        section 2. Repeat the stages I to IV.    -   6. On the last dilution take a volume A and discard them.    -   7. Obtain the digital image with the associated software against        a previously determined dark background, whose color it is        selected to increase the definition of the edges of the        aggregates.    -   8. Process the digital image with the procedures 1 to 3 proposed        below.        Procedure 1: Feature Extraction Process

For each well containing an agglutination reaction the procedure for theextraction of the features characterizes the agglutination resultperforming the following steps:

-   -   1. Apply the top-hat transform with a spherical structural        element to obtain a new image which will be referred as Rd.    -   2. Feature extraction. In this step, only pixels within the well        are considered, that is, only pixels produced by the        agglutination reaction. This set of pixels is herein referred to        as Rdi.        -   a. Compute the mean of the pixels in Rdi: Mean=mean(Rdi).        -   b. Computer the standard deviation of the pixels in Rdi:            Std=std(Rdi),        -   c. Compute other statistical and texture descriptors: Median            Absolute Deviation, Kurtosis, moments of the co-occurrence            Matrices, etc.        -   d. Estimate the area of the agglutinates by extracting the            agglutinates using a thresholding method, and estimating the            area of the agglutinates(Area=sum(A)).            Procedure 2: Obtaining the Quantification Function F.

Here, focus is on the mean feature and a quantification table iscomputed that uses the mean to obtain an automatic measure of theconcentration of the sample.

-   -   1. For each standard assay (image):        -   a. Process all standard dilutions with Procedure 1 to obtain            the agglutination features of each dilution.        -   b. Make a table with entries: Dilution, Mean, Concentration            (UI/ml).    -   2. Apply least squares to adjust a piecewise linear function to        the previously obtained table. The fitting is applied in the        region above negative response and below saturation.    -   3. Obtain safeguard thresholds that indicate the beginning an        end of the quantification zone and confidence weights.        Procedure 3: The Quantification Process (Using the Mean)

For the quantification, all dilutions of the sample are processed. Foreach dilution, the mean that characterizes the agglutination ismeasured. Suppose dilutions 1, 2, 4, 8, 16, etc. have been done and amean: m1, m2, m4, . . . , m16, . . . etc. is obtained. Also consider afunction F that maps the mean values according to their respectiveconcentrations. (see Procedure 2).

-   -   1. If mi>Th+ the dilution is declared as positive. Th+ is        obtained together with the function F and defines the values of        the feature that correspond to positive agglutinations.    -   2. Let mj be the last positive dilution.    -   3. If mj falls within the quantification region, its        concentration Q can be estimated as:        Q=F(mj)*j.  a

To validate the measure, the area of the agglutinates is used. Negativereactions have small agglutinates. Since a negative control is usedsamples can be evaluated considering the area differences. Largedifferences indicate a positive result. This must be done since thefeatures tend to saturate at high concentrations.

To make a more robust estimation, the average of all dilutions fallingwithin the quantification zone is computed. They can be determined fromthe last positive dilution and using suitable safeguard thresholdobtained together with the function F.

Calibration of the System

In these examples, the previously determined volume A corresponds but itis not limited to 60 μL and the previously determined volume Bcorresponds but it's not limited to 30 μL.

First, the results for the analysis of a set of standard samples withknown titles (IU/ml) are shown. The corresponding digital images areacquired using the proposed method. The acquired digital image isprocessed with Procedure 2 to obtain the quantification function F. Thedetails of each procedure are displayed in Table 1, contained in FIG. 5.

In FIG. 1 one of the scanned images is shown and in the results of eachdilution are shown in Table 2, contained in FIG. 6.

In each case: the image number (I, II, III . . . VII), the known IU/ml(4, 6, 8, . . . 24) is shown and the mean value according to Procedure 1is obtained.

FIG. 3 contains results for all the images with the samples from Table 1and the calibration curve F, obtained after fitting a pair of lines tothe ranges [6, 10] and [10, 24] using Procedure 2.

Examples 1-3, tables for each of which are contained in FIGS. 7-9,include the results of the analysis of samples with unknown IU/ml whichare compared with the traditional, manual, technique for reference. Withthe traditional manual technique, there is no way to find the realconcentration, because the normal proceeding takes as the concentrationof the analyte, the value of the last dilution with positiveagglutination. Actually the real value will be between the last positiveand the next two-fold dilution, so it is impossible to obtain a realvalue by application of the traditional technique. The proposed methodis able to produce more fine and accurate results.

Bach of the tables corresponding to Examples 1-3 contains the followinginformation with respect to each well: the dilution, the IU/ml obtainedby the agglutination plate method (Ref: traditional technique), thecomputed mean feature according to Procedure 1 (Mean), the IU/ml foreach dilution, the estimated IU/ml using each dilution (multiplying bythe dilution) and the estimated IU/ml using all the valid results inthird column using Procedure 3. Results are not shown for obtained valuethat fell outside of the specified linear zones, The quantization wasmade using the mean feature together with the piecewise linear functionof FIG. 3.

For each example, the estimated IU/ml obtained by this method is greateror equal to the supposed known sample value.

Example 2

Example 2 was carried out by realizing a Hemoagglutination reaction.

In this example the application of the proposed method for thesemiquantification of hemoagglutination reactions for blood typing isshown.

The traditional technique of hemoagglutination is basicallyquantitative. However, it is also useful for the hematologist to have ameasure of the score of agglutination. The agglutination reactions areclassified into five classes: negative, and four positive scores (1 to 4crosses) depending on the agglutination strength.

Sample Requirements for the Hemoagglutination Test:

Blood collected with or without anticoagulant may be used. Tests arepreferably carried out as soon as possible after collection. Samples arepreferably stored at 2-8° C. to account for possible delays inconducting the tests. Blood obtained by finger puncture may be testeddirectly by the slide method, but to avoid clotting, blood collected inthis manner should be mixed with the reagent quickly.

The temperature for carrying out the blood grouping reaction ispreferably 25° C.±5° C. and the tests are preferably not carried out at37° C. The reaction may be performed over transparent polystyreneslides, with reaction areas preferably delimited by ledges of the samematerial defining a circle.

Materials:

Orbital shaker (speed between 80 to 90 rpm).

Thermometer-Chronometer

Transparent polystyrene slides

Reagents:

1. Monoclonal agglutinating sera for the determination of Human bloodgroups

2. Normal Saline

Procedure:

1. Prepare approximately 5-10% suspension of RBCs (Red Blood Cells) innormal saline.

2. Add one drop of corresponding reagents (AntiB, AntiA, AntiAB, A forreverse grouping, B for reverse grouping, and AntiD) to the eachrespective well.

3. Add one drop of the above cell suspension (AntiB, AntiA, AntiAB andAntiD) or serum (A for reverse grouping and B for reverse grouping).

4. With separate applicator sticks, mix each cell reagent mixture well.

5. Rotate the slide at 80 rpm for 2 minutes.

6. Obtain the digital image with the associated software against apreviously determined white background.

Once a digital image is obtained, the extraction process described inthe above examples is applied. Reactions are classified as positive ornegative based on the number and area of the agglutinates. Positivereactions may be subsequently classified into four subclasses dependingon the agglutination strength, which in turns depends on the number andarea of agglutinates. Usually the technicians define four classes,identified with crosses with one cross for the weakest agglutination andfour crosses for strongest agglutinations. In some cases a plus/negativemay be used to identify border line agglutinations. With the samemethodology here describe the classification can be performed toidentify other number of classes. The quantification process is based onthe mean color of the agglutinates (other features can be used alone orin combination in order to improve the classification results).Accordingly, a semiquantification result for the hemoagglutinationreaction may obtained.

An Exemplary Embodiment of the Process Used to Obtain theSemiquantification Table F

In an exemplary embodiment, focus is placed on the mean of theagglutinates feature and computing a quantification table that is usedto obtain an automatic measure of the score of agglutination of thesample. The following quantification table is obtained using set ofsamples quantified by an ordinary skill in the art.

1. For each image in the training sample:

All reactions are processed with the disclosed extraction method todetermine the agglutination characteristics.

A table containing entries for the following properties is created:Given Agglutination Score, Feature.

2. Pattern recognition techniques are applied to find the thresholdswhich divide each agglutination class with minimum probability error. Inthis step the threshold Th+ which distinguishes positive and negativereactions is obtained.

Exemplary Semiquantification Process

For each reaction, the feature that characterizes the agglutination isobtained.

1. If mi>Th+ the dilution is deemed as positive or else the reaction isdeemed negative. Th+ is obtained together with the Table F and definesthe values of the feature that correspond to positive agglutinations.

2. For each positive reaction, the quantification table, Table F is usedto obtain an agglutination score: AS=F(mj).

In FIG. 4, a digital image of agglutination reactions is shown, in thecontext of four blood typing tests. Each column of wells contains adifferent sample. Each row contains a different reagent: row 1-anti-B,row 2-anti-A, row 3-anti-AB, row 4-alpha, row 5-beta, row 6-anti-D. Ascan be seen, the pattern of agglutination has strong variations.

Table 6, contained in FIG. 10, provides results obtained together withthe quantification given by an ordinary skill in the art. Cells shown inbrackets correspond to classification errors. These errors correspond toscore. The blood group obtained by the presently disclosed systemreflects no error.

The disclosed method was tested against 500 reactions and comprised only1% false negatives. The false negatives were all alpha or beta reactionsand therefore, it is reasonable to conclude that said false negativesdid not affect the typing result. Accordingly, 0% error in group typingwas obtained. For verification, in Table 7, contained in FIG. 11, aconfusion matrix for the score classification is shown.

As can be seen most of the samples are classified in the correct class.Most of the samples which are not classified in the correct one, shiftto the neighboring classes as expected.

Other modifications and variations to the invention will be apparent tothose skilled in the art from the foregoing disclosure and teachings.Thus, while only certain embodiments of the invention have beenspecifically described herein, it will be apparent that numerousmodifications may be made thereto without departing from the spirit andscope of the invention.

What is claimed is:
 1. A method for analyzing a digital image of anagglutination assay, the method comprising: a) performing anagglutination assay by mixing a volume of a sample dilution with acorresponding volume of at least one reactive and observing a formationof agglutinates; b) preparing a negative control by mixing a secondvolume of the sample dilution equal to the volume of sample dilution of(a) with a second corresponding volume of the reactive of (a); c)obtaining a digital image of the sample dilutions processed in (a) and(b); and d) processing said digital image against a calibration curve,and thereby generating a quantitative result value representative of adegree of agglutination of the sample and reactive.
 2. The method ofclaim 1 further comprising e) processing the image by automaticallyidentifying and quantifying the samples present in the digital image ofthe assay.
 3. The method of claim 2 further comprising: f) extracting aset of features; g) obtaining a quantification function which mapsmeasured features to the actual concentration of the sample; and h)extracting a quantitative result for each sample in the assay.
 4. Amethod of extracting a set of features that characterize a set ofagglutination results, the method comprising: a) applying a digital oroptical filter to extract a component of an image or a portion of aspectrum where a signal to noise ratio between agglutinates and abackground is maximized; b) applying a top-hat transform with aspherical structural element, thus obtaining a new image referred to asRd and assigning a set of pixels to within each well to a matrixreferred as Rdi; c) extracting pixels within a well produced by theagglutination reaction; d) computing a mean of the extracted pixels inRdi; e) computing a standard deviation of the extracted pixels in Rdi;f) computing at least one other statistical and texture descriptor; andg) determining an estimated area of the agglutinates.
 5. The method ofclaim 4, wherein the statistical and textual descriptor is at least oneof Media, Absolute Deviation, Kurtosis and moments of co-occurrenceMatrices.
 6. The method of claim 4, further comprising: obtaining asemiquantification training table F using a process comprising: h) foreach image in a training sample, processing all reactions with thefeature extraction method to obtain a set of agglutination features; andi) making a table with entries for Given Agglutination Score andFeature.
 7. A method performed by a computer comprising a centralprocessing unit (CPU) and a memory, the method being for obtaining aquantification function F for a dilution which maps a set of measuredfeatures to an actual concentration of a sample, the method comprising:a) making a table with entries comprising: Dilution, Mean andConcentration (UI/ml); b) applying a least squares analysis to a regionabove a point of negative response and below a point of saturation toadjust a parametric function to the table; c) obtaining performed by theCPU, safeguard thresholds for indicating a beginning and an end of aquantification zone and confidence weights; and d) obtaining a Th+value, that defines a set of values of a feature that corresponds topositive agglutinations.
 8. A method performed by a computer comprisinga central processing unit (CPU) and a memory, the method being forextracting a quantitative result for each dilution of a sample based onits agglutination features, a quantification function and a set ofdilutions, the method comprising: a) obtaining the quantificationfunction F for a dilution which maps a set of measured features to anactual concentration of the sample, comprising: i) making a table withentries comprising: Dilution, Mean and Concentration (UI/ml), ii)applying a least squares analysis to a region above a point of negativeresponse and below a point of saturation to adjust a parametric functionto the table; iii) obtaining performed by the CPU, safeguard thresholdsfor indicating a beginning and an end of a quantification zone andconfidence weights; and iv) obtaining a Th+ value, that defines a set ofvalues of a feature that corresponds to positive agglutinations; b) foreach agglutination feature fi for a dilution i, comparing fi with anobtained Th+ value that defines values of a feature that corresponds topositive agglutinations; c) if fi>Th+, declaring a dilution as positive;d) for a last positive dilution fj, if fj falls within a quantificationregion, estimating its concentration Q as: Q=F(fj)*j, wherein F(fj) isthe quantification function F from (a) evaluated at value fj and j isthe corresponding dilution of the sample; e) validating Th+, fj and Qvalues if Th+, fj and Q values are above a threshold of a negativecontrol and below saturation; and f) obtaining a final result bydetermining a weighted average of the validated values.
 9. A methodperformed by a computer comprising a central processing unit (CPU) and amemory, the method being of evaluating an agglutination assay, themethod comprising: a) obtaining a semiquantification table F using a setof labeled patient samples; b) applying a set of pattern recognitiontechniques to determine a set of thresholds which divide eachagglutination into an agglutination class with a minimum probabilityerror; c) obtaining performed by the CPU, a threshold Th+ which dividespositive and negative agglutination reactions; and d) for each patientsample, applying a semiquantification process for an evaluation of theagglutination.
 10. The method of claim 9, further comprising: obtaininga semiquantitative result for evaluating the agglutination assay using aprocess comprising: e) measuring a feature that characterizes theagglutination; f) obtaining a threshold (Th+); and g) for each positivereaction, using the semiquantification table F to obtain anagglutination score.